6. Conclusion

Anything can be mapped, from census data to highly sophisticated small-area estimation to expert opinion. But how good is the map? How can a poor poverty-mapping method be avoided? This is a particularly pernicious problem, given that it may be a number of years before the quality of a particular method is evident. Poverty mapping does not yet have a gold standard, partly because the context of poverty mapping is as varied as its applications. The choice of a poverty-mapping methodology therefore depends on a number of logical and legitimate considerations, discussed in Section 5, such as objectives, philosophical views on poverty, limits on data and analytical capacity and cost.

Practitioners should choose the most appropriate method for their purposes. The most disturbing problem with current poverty-mapping methods, however, is the minimal attention paid to potential error and bias, and to the types or characteristics of the poor populations chosen by different methodologies. Only two methods have made a serious attempt to gauge the importance of statistical error: the two small-area estimation methods, which provide indicators as to the error associated with increasing levels of disaggregation. Other methods, such as that used by PROGRESA, make poverty characterizations of communities as small as 50 households without specifying the statistical power of these characterizations. But data limitations often force simple solutions, such as direct census measures. In these cases, there must be an awareness of bias.

There is no evidence that methods that put themselves forward as a gold standard, particularly the World Bank small-area estimation method, result in the best poverty mapping. Indeed, there has been little study of the differences in terms of practical outcomes, error and bias between small-area estimation and other methods. Theoretically and philosophically, small-area estimation may be the best poverty-mapping method based on a consumption-based welfare indicator, but we cannot assert more then this. Alderman et al. (2000) argue that all poverty mapping comes down to appropriate weighting of a poverty index. Small-area estimation is essentially weighting an index using a multivariate regression model, and thus should compare favourably to ad hoc weights. But what about weights based on other statistical routines, or expert opinion?

Ultimately, given the lack of information regarding bias and error in most poverty-mapping methods, practitioners should proceed with full awareness of the pitfalls and uncertainties of their particular method. The robustness of the chosen method should if possible be evaluated in terms of component variables, outcome indicators and alternative methods. Further research is clearly needed in terms of comparing the statistical precision and practical outcomes of different methods. Evaluating the statistical properties of some methods may not be technically feasible, but recognizing the potential bias of each method in terms of the resulting poverty profile is an essential first step.